Instructions to use monsterapi/opt1.3B_codeinstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use monsterapi/opt1.3B_codeinstruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "monsterapi/opt1.3B_codeinstruct") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| tags: | |
| - facebook-opt-1.3b | |
| - code | |
| - instruct | |
| - instruct-code | |
| - code-alpaca | |
| - alpaca-instruct | |
| - alpaca | |
| - opt-1.3b | |
| datasets: | |
| - sahil2801/CodeAlpaca-20k | |
| base_model: codellama/CodeLlama-7b-hf | |
| We finetuned Facebook/OPT-1.3B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). | |
| This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. | |
| The finetuning session got completed in 1 hour and 30 minutes and costed us only `$6` for the entire finetuning run! | |
| #### Hyperparameters & Run details: | |
| - Model Path: facebook/opt-1.3b | |
| - Dataset: sahil2801/CodeAlpaca-20k | |
| - Learning rate: 0.0003 | |
| - Number of epochs: 5 | |
| - Data split: Training: 90% / Validation: 10% | |
| - Gradient accumulation steps: 1 | |
| --- | |
| license: apache-2.0 | |
| --- | |